Dynamics of digital tourism’s consumers in the EU · 2019-10-01 · Luis Manuel Ruiz‑Gómez1 ·...

Vol.:(0123456789)

Information Technology & Tourismhttps://doi.org/10.1007/s40558-018-0124-9

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ORIGINAL RESEARCH

Dynamics of digital tourism’s consumers in the EU

Luis Manuel Ruiz‑Gómez1 · Julio Navío‑Marco1 · Luisa Fernanda Rodríguez‑Hevía2

Received: 8 May 2018 / Revised: 5 October 2018 / Accepted: 2 November 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018

AbstractThis article examines the effects of the new e-citizens’ digital skills in their behav-iour as e-tourists. It analyses how European digital citizens behave in digital tour-ism, and whether there are any geographical differences. We have observed that the most influential factor for these e-Tourism activities is having made purchases via the Internet in the past 12 months. Differences have been detected between the more developed and less developed areas of Europe, which could indicate a digital divide. Therefore, the results indicate divergent behaviour patterns in digital travel and accommodation, as well as divergent trends in different EU geographical areas.

Keywords Internet · ICT · eTourism · Consumer behaviour · EU · Digital tourism

1 Introduction

Digitalisation and use of the Internet are changing the structure of the tourism industry, altering the entry barriers, facilitating price transparency and competition, revolutionising the distribution channels, optimising cost and improving production efficiency (Kim et al. 2004; Buhalis and O’Connor 2005; Buhalis and Law 2008). In fact, the tourism industry has become the biggest category for products and services sold via the Internet (Abou-Shouk et al. 2013).

* Luis Manuel Ruiz-Gómez [email protected]

Julio Navío-Marco [email protected]

Luisa Fernanda Rodríguez-Hevía [email protected]

1 Department of Management and Business Organization, UNED, Paseo Senda del Rey N11, 28040 Madrid, Spain

2 Department of Business Economy, Alfonso X El Sabio University, Villanueva de la Cañada, Spain

http://orcid.org/0000-0002-4607-326X

http://crossmark.crossref.org/dialog/?doi=10.1007/s40558-018-0124-9&domain=pdf

L. M. Ruiz-Gómez et al.

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It is becoming increasingly frequent for digital citizens to query and obtain the information they require and to prepare their travelling experiences via the Inter-net or electronic devices (Chung and Koo 2015; Filieri and McLeay 2014). Apart from obtaining this information, they also manage the selected e-tourism ser-vices, such as booking hotels, air tickets and purchasing their trips via the Inter-net or mobile devices (Amaro and Duarte 2015; Kim et al. 2013; Navío-Marco et al. 2018; Wang et al. 2016; Suki and Suki 2017). This analysis will focus on these search, purchase and management of tourism activities to obtain a better understanding of the tourism product demand and purchase phase.

Our study is of special interest from the search for trip information to comple-tion of the purchasing process, because knowing the customers and their habits is a vital success factor when deploying ICTs in tourism. According to Warschauser (2004), what is most important about ICT is not so much the availability of com-puting devices or the Internet line, but rather people’s ability to use such devices to engage in meaningful social practices.

Early findings from different academic studies indicated that Internet users shared one single sociodemographic profile: In the 90s, an internet user was probably under 45 and had a University Degree (Weber and Roehl 1999). More recently, Sigala (2004) indicates that the sex, age, education level, career, eco-nomic profile, ethnic group and certain Internet skills are common to all users. However, this author also points out that the impact of social factors on e-com-merce is under researched not only in the general literature but also in the context of e-travel and e-tourism in particular (Sigala 2004).

Users’ gradual immersion in the digital world and their maturity and aging in this environment could have modified their standard profile and behaviour, mak-ing it advisable to conduct further research into the question. In fact, the sen-ior group constitutes the “engine of growth” in tourism (Schröder and Widmann 2007; Chen and Shoemaker 2014), and very little research has been done into this group’s more specific digital behaviour (Niehaves and Plattfaut 2014).

Furthermore, hardly any research draws the attention to the geographical aspect of the sociodemographic profile of the European digital tourists. When compared to studies in other environments such as Australia (Mistilis et al. 2014) or China (Guo et al. 2014), it is clear that there are very few studies on a pan-European level (Szopiński and Staniewski 2016).

This study provides answers to the following research questions: what is the profile of the tourist in Europe where digital technology use is concerned? Are there any differences in the European environment? We need to know not only who these digital customers are, but also to understand what impact their charac-teristics have on the e-tourism business.

The findings obtained show not only the importance of having purchased other goods and services (not associated with tourism) via the Internet in the past 12 months, but also the major role of senior tourists in the different regions. In this sense, our contribution is mainly twofold: firstly, we develop e-tourism lit-erature, increasing an understanding of the e-tourist, through an empirical com-parative analysis at European level; secondly, we provide insights into the driving

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forces behind e-tourists’ behaviour, highlighting the influence of previous pur-chases via the Internet (unrelated to tourism) and the role of the senior segment.

2 Literature review: e‑tourist digital capacities

An increasing amount of literature has analysed the impact of ICTs on the tourism ecosystem, as can be seen from the extensive bibliographical reviews (Frew 2000; Leung and Law 2007; Buhalis and Law 2008; Law et al. 2009, 2014; Pesonen 2013; Ukpabi and Karjaluoto 2017). Four main research areas can also be established from the numerous research works studying the different ways of linking ITC technol-ogies and tourism from a user perspective. These areas also involve a direct link between digital devices and tourists, and are as follows: (1) the technological tools and devices utilised in tourism, (2) the information retrieved and exchanged, (3) the interaction and flow between the tool or application and the user, (4) the customers’ profiles. Table 1 details some relevant researches, by way of example, for the differ-ent areas.

Focusing on the fourth fundamental line, centred specifically on the customer, a wide theoretical corpus has been used to address the behavioural determinants asso-ciated with the adoption and use of the Internet and ICTs in general.

The literature presents well-grounded theories about customer behaviour, namely the Theory of Reasoned Action (TRA) (Fishbein and Ajzen 1975), the Theory of Planned Behaviour (TPB) (Ajzen 1991), the Innovations Diffusion Theory (IDT) (Rogers 1995) or the Technology Acceptance Model (TAM) (Davis 1989). These theories have received substantial empirical support in explaining users’ acceptance in several domains, notably information systems, and, specifically, online shop-ping (Amaro and Duarte 2013, for a literature review). The Technology Acceptance Model (TAM), which mainly originates from the information systems field, has been subject to subsequent theory development (Venkatesh and Davis 2000), especially the Unified Theory of Acceptance and Use of Technology (UTAUT) and Model of Adoption of Technology in Households (MATH).

Specifically with tourism in mind, Fleischer and Felsenstein (2004) pointed out that research analysing travel on the Internet uses two different approaches, one of which focuses on changes regarding suppliers, i.e., traditional agents, new services, and new brokers and platforms. The other approach concentrates on the social and economic profiles of Internet users, and their attitude towards on-line tourism ser-vices. As far as e-tourism is concerned, the background to on-line purchases could be classified into three main categories: characteristics of the consumer, character-istics of the perceived channel and characteristics of the website and the product (Amaro and Duarte 2013).

Sociodemographic characteristics influence the behaviour associated with the purchase of on-line tourism services. The actual purchase of a product is determined by age, education, income and profession (Morrison et al. 2001). There are also cases of e-tourism research into the role of personal factors affecting the e-tourist such as age (e.g., older travellers that use ICTs, in Pesonen et al. 2015) sex (Kim et al. 2013) or sexual orientation factors (for example, homophilia in Ayeh et al.


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Tabl

e 1

Rel

atio

nshi

p be

twee

n th

e te

chno

logy

and

the

tour

ist

Are

aTo

pic

Exam

ples

in th

e lit

erat

ure

Mai

n fin

ding

Tech

nolo

gica

l too

ls

and

devi

ces

Type

of t

echn

olog

yIn

tern

et (C

ardo

so a

nd L

ange

200

7; F

odor

and

Wer

thne

r 20

05),

mob

ile p

hone

s (Li

burd

200

5; K

im e

t al.

2015

), so

cial

net

wor

ks (H

arrig

an e

t al.

2017

, Chu

ng a

nd K

oo

2015

), In

tern

et o

f thi

ngs (

Kau

r and

Kau

r 201

6), N

FC

(Kim

and

Kim

201

7), s

atel

lite

(Vija

y et

al.

2016

), vi

rtual

an

d au

gmen

ted

real

ity (K

ouna

vis e

t al.

2012

) wea

rabl

es

(Jha

jhar

ia e

t al.

2014

), ro

bots

(Mur

phy

et a

l. 20

17)

Seve

ral i

nves

tigat

ions

ana

lyse

the

spec

ifics

of t

he in

tera

ctio

n be

twee

n th

e cu

stom

ers a

nd th

e te

chno

logy

und

er st

udy

Func

tiona

litie

sK

apla

nido

u an

d Vo

gt (2

006)

The

resu

lts sh

owed

that

mot

ivat

ing

visu

als a

nd tr

ip in

form

a-tio

n fu

nctio

nalit

y w

ere

sign

ifica

nt p

redi

ctor

s of t

ouris

m

Web

site

use

fuln

ess.

Web

site

use

fuln

ess w

as a

sign

ifica

nt

pred

icto

r of i

nten

t to

trave

l to

the

desti

natio

nD

esig

nLa

w e

t al.

(201

0)Th

e au

thor

s hig

hlig

ht th

e im

porta

nce

of w

ebsi

te d

esig

n an

d th

at a

num

ber o

f web

site

dev

elop

men

t eva

luat

ion

instr

u-m

ents

hav

e ap

pear

ed in

the

tour

ism

fiel

dN

avig

abili

tyH

erre

ro a

nd S

an M

artín

(201

2)In

form

atio

n on

the

acco

mm

odat

ion

and

desti

natio

n po

si-

tivel

y in

fluen

ces t

he p

erce

ived

use

fuln

ess o

f the

web

s, w

here

as b

oth

the

inte

ract

ivity

and

nav

igab

ility

hav

e a

posi

-tiv

e eff

ect o

n th

e pe

rcei

ved

ease

of u

se o

f the

web

site

sIn

form

ativ

ityLa

i (20

15)

Two

fact

ors i

nflue

ncin

g us

e of

mob

ile A

pp fo

r tou

rism

are

in

form

ativ

enes

s and

ent

erta

inm

ent

Info

rmat

ion

retri

eved

and

ex

chan

ged

Acc

urac

y/re

liabi

lity

Filie

ri an

d M

cLea

y (2

014)

, Chu

ng a

nd K

oo (2

015)

Info

rmat

ion

accu

racy

, inf

orm

atio

n va

lue-

adde

d, in

form

atio

n re

leva

nce,

are

stro

ng p

redi

ctor

s of t

rave

lers

’ ado

ptio

n of

in

form

atio

n fro

m o

nlin

e re

view

s on

acco

mm

odat

ions

1 3


Tabl

e 1

(con

tinue

d)

Are

aTo

pic

Exam

ples

in th

e lit

erat

ure

Mai

n fin

ding

The

inte

ract

ion

and

flow

with

the

user

Com

plex

ityA

mar

o an

d D

uarte

(201

5)In

divi

dual

s’ p

erce

ived

com

plex

ity o

f onl

ine

trave

l sho

ppin

g is

neg

ativ

ely

rela

ted

to a

ttitu

de to

war

ds o

nlin

e tra

vel s

hop-

ping

Nov

elty

Che

n et

al.

(201

4)N

ovel

ty in

a b

log

affec

ts b

ehav

iour

al in

tent

ion

to v

isit

Enjo

ymen

tC

hung

and

Koo

(201

5)Th

e us

ers o

f new

soci

al m

edia

, esp

ecia

lly fo

r tra

vel i

nfor

ma-

tion

sear

ches

, are

influ

ence

d by

bot

h be

nefit

s (in

form

a-tio

n re

liabi

lity,

enj

oym

ent)

and

sacr

ifice

s (co

mpl

exity

, pe

rcei

ved

effor

t)Sa

fety

Kim

et a

l. (2

006)

, Esc

obar

-Rod

rígue

z an

d C

arva

jal-T

rujil

lo

(201

4)C

onsu

mer

s of l

ow-c

ost a

irlin

es w

ho p

urch

ase

ticke

ts o

nlin

e ar

e in

fluen

ced

mos

tly b

y th

eir t

rust

and

habi

ts in

usi

ng

such

web

site

s. Sa

fety

as f

acto

r infl

uenc

ing

Chi

nese

onl

ine

rese

rvat

ions

inte

ntio

nsC

usto

mer

Profi

le, a

ttitu

des a

nd

inte

ntio

nsA

gag

and

El-M

asry

(201

6)C

onsu

mer

s’ in

tent

ions

to b

ook

hote

l onl

ine

are

dete

rmin

ed

by c

omm

itmen

t, tru

st, a

ttitu

de, a

nd th

eir a

ntec

eden

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Fina

lly, c

omm

itmen

t, tru

st an

d at

titud

e ha

ve h

ighe

r in

fluen

ce o

n in

tent

ion

to b

ook

hote

l onl

ine

for l

ow-h

abit

custo

mer

sB

ehav

iour

Am

aro,

and

Dua

rte (2

015)

Perc

eive

d be

havi

oura

l con

trol p

ositi

vely

influ

ence

s int

en-

tions

to p

urch

ase

trave

l onl

ine,

ech

oing

the

postu

latio

n of

th

e Th

eory

of P

lann

ed B

ehav

iour

Invo

lvem

ent w

ith th

e te

chno

logy

Ukp

abi a

nd K

arja

luot

o (2

017)

Con

sum

ers’

em

otio

nal i

nvol

vem

ent,

attit

ude,

inno

vativ

enes

s an

d flo

w a

re im

porta

nt p

erso

nal c

hara

cter

istic

s for

the

purc

hase

of t

rave

l onl

ine

Perc

eptio

n of

self-

effica

cySr

ivas

tava

and

Dha

r (20

16)

Resu

lts in

dica

ted

a po

sitiv

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fluen

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f Int

erne

t sel

f-effi

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on

job

perfo

rman

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hen

trave

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nt e

mpl

oyee

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er th

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usto

mer

s bet

ter i

nfor

mat

ion

thro

ugh

blog

s and

po

sts o

n th

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tern

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nd u

se th

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tern

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sour

ce o

f in

form

atio

n


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(2013). The studies of gender influence, when analysing internet purchasing behav-iours (Cabezudo et al. 2004; Drèze and Hussherr 2003; Margarida 2013) and Tour-ism 2.0 (Hernández-Méndez and Muñoz-Leiva 2015) report mixed results.

Li and Buhalis (2006) revealed that (1) age was a factor where on-line purchase intention was concerned, and (2) individuals between 31 and 40 years old were more likely to purchase in this context. New studies have been published about the specific nature of the new generations of tourists: Silent Generation, Baby Boomers, Gen-eration X, and Generation Y (Li et al. 2013). Bilgihan et al. (2013) and Parsa and Cobanoglu (2011) highlight the pivotal role of affective commitment for develop-ing and maintaining long-term relationships in tourism with Generation Y. Research into the behaviour and impact of the millennials is also on the increase (Ramsay et al. 2017). Although tourists’ online behaviour has been increasingly studied where the younger part of the population is concerned (Nusair et al. 2012), less work has been done in the elderly tourist segment. The senior collective deserves more atten-tion, because it is a growing and lucrative category of tourists (Mahadevan 2014; Chen and Shoemaker 2014).

Heung (2003) observed that travellers mainly from Western Countries with higher levels of education and higher annual household incomes are more likely to use the Internet to purchase travel services (Heung 2003). They are also more likely to have had prior travelling experience, which also makes them more receptive to the on-line booking of travel purchases (Gursoy and Chen 2000).

Finally, it is observed that the absence of connectivity (bandwidth restrictions) is a factor that limits the use of e-commerce, and, thus, e-tourism (Buhalis and Jun 2011; Turban et al. 2008). Broad bandwidth and, in general, proper connectivity is critical for allowing the transmission of digital information at high bandwidths on existing phone lines for supporting the users (Buhalis and Licata 2002).

In summary, as has already been pointed out, many studies have been conducted with a view to understanding the profile, attitude and behaviour of e-tourists, but only a few associate these activities with extensive geographical areas. Such research could yield important general information that is not limited to one particu-lar country or city, allowing us to establish comparisons. We have also observed that the senior tourist market has received little attention from tourism literature and that different generations of e-tourists behave in different ways.

3 Methodology and data

As the data sample, we have used the information obtained from the questionnaire: “European Union survey on ICT usage in households and by individuals 2015” (Eurostat Model Questionnaire Version 3.1.). This survey is conducted on a yearly basis in European Union countries. Information is thus obtained from the EU inhab-itants who use the Internet about how they use e-commerce. As the aim is to study the characteristics of the users that have purchased goods and services associated with tourism via the Internet (holiday accommodation and other services for travel-ling) in the last 12 months, the study target has been selected from individuals who

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have used the Internet in the past 12 months, which in this case amounts to 150,035 persons.

A binary logistic regression model was devised to establish the profile of con-sumers who book accommodation and travel via the Internet. This model was cho-sen because it enables the user to explain or predict the characteristic of a dichoto-mous event on the basis of a series of variables that are considered to have a bearing (López-Roldan and Fachelli 2015).

One of the objectives is to compare to see whether this profile changes on the basis of the geographical location where the respondents live. In this sense, the logistic regression model is applied in three types of zones. We use the NUTS clas-sification created by the EU that reflects the territorial administrative division of the Member States.1 The NUTS classification is the most used for studies since they are the basic regions for the planning of regional and cohesion policies of the Euro-pean Union, as they are observed in different studies (Fischer and Stumpner 2008; Von Lyncker and Thoennessen 2017; Overman and Puga 2002). The use of regional data (NUTS) instead of national data is recommended as national studies can hide regional disparities (Overman and Puga 2002). Eurostat database provides also a GDP level per region (GEO_DEV), for the logistic regression:

• Area 1: Less Developed Region. Where the GDP per inhabitant is less than 75% of the average for the EU-27.

• Area 2: Region in Transition. Where the GDP per inhabitant ranges from 75 to 90% of the average for the EU-27.

• Area 3: More Developed Region. Where the GDP per inhabitant is higher than 90% of the average for the EU-27.

A binary logistic regression model has been estimated to analyse the purchase of holiday accommodation and travel via the Internet and the determining factors that have an effect on each one of the groups on the basis of their economic develop-ment, in order to establish the differences between the influencing variable when it comes to making the decision to purchase these goods and services associated with tourism. The aim when conducting this analysis was to have a minimum target of 10,000 respondents for each one of the models. As the sample sizes for our target range greatly from one region to another, the sampling fractions were applied for each zone that is shown in Table 2. A simple random sample was carried out for each one of the groups, to make sure that the significance of the variables was not due to the sample size.

The dependent variable is dichotomy, taking the value 1 when the respondent has made on-line purchases of goods and services associated with tourism in the past 12 months, and taking the value 0 if not.

The variables explaining the choice of digital purchase are grouped into sociode-mographic aspects, the type of connection that they use to access the Internet, the use that is made of the Internet, the digital skills they possess, the origins of the

1 See http://ec.europ a.eu/euros tat/web/regio ns.

http://ec.europa.eu/eurostat/web/regions


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seller and the frequency of purchase. To make this easier to understand, a descrip-tion of the variables is given in Table 3 below.

Statistical software SPSS Version 24 was utilised to devise the binomial logis-tic regression model with the aforementioned data for each one of the zones distin-guished on the basis of economic development.

To begin with a logical regression was carried out involving the entire study tar-get, in which apart from the aforementioned variables, the development variable for the region was also included for explanatory purposes. It could be deduced from the results of the regression that this variable is statistically significant for each one of the categories analysed, and that if we take the Less Developed Regions as the refer-ence category, any movement towards More Developed Regions indicates a greater likelihood of being a digital consumer of tourist products (2.48 times for Regions in Transition and nearly three times more likely for the More Developed Regions. Table 4 shows the marginal findings of the regression for the regional development variable.

4 Findings and discussion

4.1 Findings for the estimation in the Less Developed Regions

A simple random sample of 10,933 respondents was selected for the regions with a low level of economic development, and these were subjected to logistic regression. The findings can be seen in Table 5. A square R of 23% was obtained from Cox and Snell, a square R of 43.8% was obtained from Nagelkerke, as well as a value for p of 0.805 in the Hosmer and Lemeshow Test. The regression was classified correctly at 88.5% for the general cases, and the classification for the digital purchasers of tour-ist services and goods was correct at 27.8%, being 97.0% for non-purchasers (the cut-off point is 0.5). In view of all the above, it can be guaranteed that the logistic regression is well adjusted. The coefficients (B) are positive for all the significant variables, so an increase in each one of them, when compared to the reference cat-egory, is tantamount to an increase in the likelihood of purchasing tourism products on the Internet.

Table 2 Number of cases in each development region

Source: own research and from the “European Union survey on ICT usage in households and by individuals 2015”*Simple random sample

Zone Number of cases Fraction of sample applied* (%)

Less developed regions 10,933 20Regions in transition 10,164 70More developed regions 12,020 15Total 33,117

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When we interpret the odds ratios (Exp(B)) and begin to analyse the socioeco-nomic variables, we find that age is the major variable, especially with respond-ents who are between 55 and 64 years old. This age group further increases its likelihood of being on-line tourism purchasers when compared to respondents in the 16–24 year age range. Persons who live in densely populated areas are more likely to make an on-line purchase of this type of product than those living in sparsely populated zones (1.314 times more). People with a higher education level are also more likely to purchase than those with a primary education level (2.117 times more).

Table 3 Independent variables and modalities

Source: own research using “European Union survey on ICT usage in households and by individuals 2015”

Variable Reference category Rest of categories

Sociodemographic Population density Sparse Intermediate

Dense Age From 16 to 24 years From 25 to 34 years

From 35 to 44 yearsFrom 45 to 55 yearsFrom 55 to 64 years65 years or over

Education level Primary education Secondary educationHigher education

Sex Male Female Employment Unemployed Employed or self-employed

InactiveConnection to the Internet Fixed broadband at home No Yes Mobile broadband at home No Yes Mobile network outside home and work

via mobile networkNo Yes

Wi-Fi outside home or work No YesUse of the Internet Seeking information No Yes Communication No Yes Social Media No Yes E-commerce No Yes E-government No Yes E-learning No Yes

Origin of the seller Outside the EU No Yes

Digital skills Digital skills None or low level Basic level

Higher level


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Those who connect to the Internet outside the home or work using a mobile net-work are 1.534 times more likely to purchase than those who do not make this type of connection. Furthermore, those who have a broadband connection at home are 1.589 times more likely to purchase these products than those who do not.

Having made an on-line purchase of other goods and services unrelated to tour-ism in the past 12 months stands out among the different uses to which the Inter-net is put. Making such purchases increases the likelihood of tourism purchases by 17.187. This is the most influential variable.

Having purchased from a seller outside the EU makes it more likely that such persons will purchase tourism services than not purchase them (1.799 times more probable).

4.2 Findings for the estimation in the regions in transition

In this case, 10,164 respondents were selected from a single random sample and the logical regression was estimated. A Cox and Snell R-squared adjustment of 31.2% and a Nagelkerke R-squared adjustment of 44% were obtained, and the value yielded for the Hosmer and Lemeshow Test was 0.937. This model managed to cor-rectly classify 78.5% of the global, 63.7% of the digital purchasers of tourism and 85.2% of the non-purchasers (the cut-off point is 0.5). In view of the results we can rest assured that the adjustment is good for the logistic regression. The findings are shown in Table 6.

The (B) coefficients are positive for all the significant variables, so an increase in each one of them, in relation to the reference category, is going to mean an increase in the likelihood of purchasing tourism products via the Internet.

On interpreting the odds ratios (Exp(B)), as part of the sociodemographic vari-ables, it must be mentioned that the respondents who are 65 or over are 3.677 times more likely to be digital consumers of tourism than those in the 16–24 year bracket.

Table 4 Marginal findings of the logistic regression for geographical location

Source: own research using the “European Union survey on ICT usage in households and by individuals 2015”*Reference category

Dependent variable: purchasing accommo-dation and travel via the Internet

Exp (B) 95% CI for EXP(B) P value

Lower Higher

Geographical location Less developed regions* 0.005 Regions in transition (1) 2.481 2.350 2.619 0.000 More developed regions (2) 2.962 2.857 3.071 0.000 Number of cases 150.035 Cox and Snell’s pseudo R2 0.352 Nakelkerke’s pseudo R2 0.511 Cut-off point 0.5 % of cases correctly classified 82%

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Table 5 Findings for logistic regression in less developed regions

The variables that are statistically insignificant in all categories are not reported in the tableSource: own research using the “European Union survey on ICT usage in households and by individuals 2015”*Reference category

Dependent variable: purchasing accommodation and travel via the Internet

Exp(B) 95% CI for EXP (B) P value

Lower Higher

Population density Sparse* 0.005 Dense (1) 1.314 1.115 1.549 0.001 Intermediate (2) 1.170 0.981 1.395 0.080

Age From 16 to 24 years* 0.000 From 25 to 34 years (1) 1.309 1.016 1.686 0.037 From 35 to 44 years (2) 1.657 1.275 2.155 0.000 From 45 to 54 years (3) 1.969 1.484 2.613 0.000 From 55 to 64 years (4) 2.237 1.641 3.050 0.000 65 years or over (5) 1.987 1.284 3.075 0.002

Educational level Primary* 0.000 Secondary (1) 1.502 1.111 2.030 0.008 Higher (2) 2.117 1.540 2.911 0.000

Connection to the Internet Mobile broadband (yes) 1.472 1.269 1.708 0.000 Fixed broadband (yes) 1.589 1.240 2.035 0.000 Connection to the Internet via mobile network or

USB outside the home in last 3 months (yes)1.534 1.310 1.798 0.000

Connection to the Internet by Wi-Fi outside the home in last 3 months (yes)

1.432 1.227 1.672 0.000

Digital skills global None or low level* 0.000 Basic level (1) 1.620 1.278 2.053 0.000 Higher level (2) 2.411 1.888 3.079 0.000

Use of the Internet E-commerce in last 12 months (yes) 17.187 13.395 22.053 0.000 E-government in last 12 months (yes) 1.726 1.462 2.039 0.000 E-learning in last 12 months (yes) 1.188 1.032 1.366 0.016

Seller’s origin: purchase 12 months Seller outside the EU (yes) 1.799 1.506 2.149 0.000

Constant 0.000 0.000Number of cases 10.933Cox and Snell’s pseudo R2 0.23Nakelkerke’s pseudo R2 0.438Cut-off point 0.5% of cases correctly classified 88.5Hosmer and Lemeshow test 0.805


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Having a higher level of digital skills makes an individual 1.943 times more likely to be a digital consumer of tourism than a person with a low or negligible level of digital skills.

It must be pointed out that having made on-line purchases increases by 9.478 the likelihood of purchasing tourism products when compared to those who have not done so, this variable being the most influential one.

4.3 Findings for the estimation in the most economically developed regions

A total of 12,020 respondents were selected by simple random sampling to conduct the logistic regression analysis. The findings can be seen in greater detail in Table 7. A Cox and Snell R-squared of 36.1% and a Nagelkerke R-squared of 49.5% were obtained, and the value of p for the Hosmer and Lemeshow Test was 0.296. The fact that the latter was greater than 0.05, is indicative of the goodness of the adjustment (Rodríguez and Gutiérrez 2007) Furthermore, this model managed to correctly clas-sify 79% of the global, 75% of the digital purchasers of tourism and 81.2% of the non-purchasers (the cut-off point is 0.5).

Having made on-line purchases in the past 12 months is the most influential vari-able when it comes to increasing the likelihood of purchasing products associated with tourism, given that doing so is 16.880 times more probable than if one has not been directly involved in e-commerce activities. In this sense, if they have indulged in e-government activities, it is 2.004 times more likely that they will purchase prod-ucts associated with tourism than if they have not done so.

The (B) coefficients are positive for all the significant variables, so an increase in each one of them, in relation to the reference category, is going to mean an increase in the likelihood of purchasing tourism products via the Internet.

On interpreting the odds ratios (Exp(B)), the respondents who are 65 years old or more are 3698 times more likely to purchase these types of goods or services than those in the 16–24 year bracket.

With regard to both the level of education and the level of digital skills, and tak-ing the lowest levels as the reference, the likelihood of purchase increases (2.101 for higher education when compared to primary education and 2.003 for greater digital skills when compared to those whose skill level is low or negligible).

5 Discussion

By comparing the results of the logistic regression applied to each one of the mod-els, it can be seen in Table 8 that the fact individuals use the Internet for commu-nication purposes or to indulge in social media activities, does not mean they are going to be more likely to purchase online tourism products in any of the regions analysed.

There are some differences between the regions considered regarding the vari-ables that were included in the analysis and those that are statistically significant in all or some of the categories taken into account.

1 3


Table 6 Findings for logistic regression in the regions in transition


Exp (B) 95% CI for EXP (B) P value

Lower Higher




Employment Unemployed* 0.000 Employed or self-employed (1) 2.146 1.758 2.620 0.000 Inactive (2) 1.451 1.151 1.828 0.002

Connection to the Internet Fixed broadband (yes) 1.234 1.044 1.459 0.014 Connection to the Internet via mobile network or

USB outside the home in last 3 months (yes)1.187 1.048 1.344 0.007


1.613 1.435 1.814 0.000


Use of the Internet Looking for information in last 3 months (yes) 1.615 1.216 2.145 0.001 E-commerce in last 12 months (yes) 9.478 8.083 11.115 0.000 E-government in last 12 months (yes) 1.963 1.739 2.216 0.000

Seller’s origin: purchase 12 months Seller outside the EU (yes) 1.477 1.276 1.709 0.000

Constant 0.002 0.000 Number of cases 10.164 Cox and Snell’s pseudo R2 0.312 Nakelkerke’s pseudo R2 0.44 Cut-off point 0.5

% of cases correctly classified 78.5Hosmer and Lemeshow test 0.937


1 3

Regarding the population density in the zone where the usual home is, the inter-mediate density category has no effect in any of the regions, whereas in the case of densely populated zones, the likelihood of being a purchaser of tourist products via the Internet is greater in the Less Developed Regions than in the rest of the Regions studied (1.3 times more than in sparsely populated zones, whereas in the rest of the regions this value is 1.2).

As far as the age variable is concerned, as age increase so does the likelihood of being a digital tourist in all the Regions. However, in the Less Developed Regions, the category that shows the most significant increase is among individuals who are between 56 and 64 years old (an odds ratio of 2.377), there being a decrease for indi-viduals over 65. However, this behaviour is not the same in the rest of the Regions, given that for individuals over 65 the increase continues, making it the category where individuals are most likely to purchase via the Internet, although it is true to say that the odds ratio among them is virtually the same (3.677 for the Regions in Transition and 3.698 for the More Developed Regions).

The most outstanding aspect of the sex variable is that being a woman increases the probability of deciding to purchase tourism via the Internet only in the More Developed Regions, whereas this variable is not statistically significant for the rest of the Regions, so no distinctions can be made where this variable is concerned.

The behaviour of digital tourism consumers is the same for all the development regions. As the levels rise in relation to the reference category there is an increase in the likelihood of purchase, the values being very similar in all the regions.

The employment level does not affect the decision to purchase products via the Internet in the Less Developed Regions, whereas in the rest of the Regions the behaviour is different. In the More Developed Regions what has an effect is that the individual is in the employee or the self-employed bracket, whereas in the Regions in Transition all the categories are significant.

Broadband connection to the Internet at home does not have an effect on purchas-ing via the Internet in the Developing Regions (regardless of whether the connection is fixed or mobile), and in the Regions in Transition only having fixed broadband makes a difference, whereas in the Less Developed Regions both types of connec-tion in the home increase the likelihood of tourism products being consumed. Con-nections to the Internet outside the home or work are influential variables in all the Regions, but consumer behaviour is different. Connection via mobile network or USB has a greater influence in the Less Developed Regions, whereas Wi-Fi connec-tion is more influential in the More Developed Regions.

Higher levels of global digital competence increase the probability of e-tourism purchase in all regions. However, it must be pointed out that in the Less Developed

Table 6 (continued)The variables that are statistically insignificant in all categories are not reported in the tableSource: own research using the “European Union survey on ICT usage in households and by individuals 2015”*Reference category

1 3


Table 7 Findings for logistic regression in the most economically developed regions



Lower Higher



Sex Male* Female (1) 1.212 1.100 1.335 0.000


Employment Unemployed* 0.000 Employed or self-employed (1) 1.830 1.473 2.273 0.000 Inactive (2) 1.202 0.946 1.527 0.132

Connection to the Internet Connection to the Internet via mobile network

or USB outside the home in last 3 months (yes)

1.298 1.160 1.452 0.000


1.551 1.392 1.729 0.000


Use of the Internet Looking for information in last 3 months (yes) 1.498 1.133 1.980 0.005 E-commerce in last 12 months (yes) 16.880 14.362 19.840 0.000 E-government in last 12 months (yes) 2.004 1.789 2.245 0.000 E-learning in last 12 months (yes) 1.131 1.011 1.265 0.032

Seller’s origin: purchase 12 months Seller outside the EU (Yes) 1.745 1.523 2.000 0.000

Constant 0.002 0.000Number of cases 12.020Pseudo R2 de Cox y Snell 0.361


1 3

Regions, the effect on likelihood is greater than in the rest of the Regions (2.4 more likely, compared to 1.94 and 2.00 for the rest).

E-commerce stands out where Internet use is concerned. This is the most influen-tial variable, the difference being much more significant than the rest of the variables analysed. Behaviour also varies greatly from one Region to another when it comes to the effect of influence on probability. An increase in the likelihood of purchasing tourism products via the Internet is very similar for the Less and More Developed Regions, the values being very similar (17.2 and 16.7, respectively), amounting to a rise of 80% when compared to the Regions in Transition (9.48).

Purchasing from a seller whose is based outside the European Union also increases the likelihood of purchasing tourism products in all the Regions, but less so in the Regions in Transition and more so in the Less Developed Regions, albeit with values very similar to those for the More Developed Regions.

If we take into account the odds ratios for the variables that are significant for each one of the regions, we can create a profile for the online purchaser of tourism products for each one of the zones studied. When the variables fall into more than two categories, what has been taken into account is the category with the greatest influence (odds ratio). Table 8 shows these variables for each one of the Regions on the basis of the influence (advantage) that each one has, in order to establish the pro-file in each one of the Regions. The table also arranges them in order of influence, from greatest to least.

If the variables that amount to a marginal effect on the increase of likelihood of purchasing tourism via the Internet equivalent to or greater than two (odds ratio) are considered for all the Regions, the variables that stand out are e-commerce, age (with a different age bracket for the Less Developed Regions) and higher education level. The digital competence level lies in this group for the Less or More Developed Regions, and the Regions in Transition have a very close value. Using the Inter-net for e-government, doubles the likelihood in the More Developed Regions and almost doubles it in the Regions in Transition.

Apart from a genuine characterisation of the e-tourists’ behaviour and use of ICTs at a theoretical level, the main contributions made by this research are sum-marized in the conclusions.

Table 7 (continued)



Lower Higher

Population density 0.495Sparse* 0.5Dense (1) 79Intermediate (2) 0.296

The variables that are statistically insignificant in all categories are not reported in the tableSource: own research using the “European Union survey on ICT usage in households and by individuals 2015”*Reference category

1 3


Tabl

e 8

Com

parin

g th

e lo

gisti

c re

gres

sion

resu

lts in

the

deve

lope

d re

gion

s

Dep

ende

nt v

aria

ble:

pur

chas

ing

acco

m-

mod

atio

n an

d tra

vel v

ia th

e In

tern

etLe

ss d

evel

oped

regi

ons

Regi

ons i

n tra

nsiti

onM

ost d

evel

oped

regi

ons

Odd

s rat

ioW

eigt

h or

der*

*O

dds r

atio

Wei

gth

orde

r**

Odd

s rat

ioW

eigt

h or

der*

*

Popu

latio

n de

nsity

Spa

rse*

Den

se (1

)1.

314

11º

1.19

711

º1.

198

12º

Inte

rmed

iate

(2)

––

–A

ge Fro

m 1

6 to

24

year

s* F

rom

25

to 3

4 ye

ars (

1)1.

309

1.45

61.

319

Fro

m 3

5 to

44

year

s (2)

1.65

71.

858

1.78

3 F

rom

45

to 5

4 ye

ars (

3)1.

969

2.16

31.

97 F

rom

55

to 6

4 ye

ars (

4)2.

237

3º2.

632

2.39

8 6

5 ye

ars o

r ove

r (5)

1.98

73.

677

2º3.

698

2ºSe

x Fem

ale

(yes

)–

–1.

212

11º

Educ

atio

nal l

evel

Prim

ary*

Sec

onda

ry (1

)1.

502

1.63

11.

44 H

ighe

r (2)

2.11

74º

2.38

23º

2.10

13º

Empl

oym

ent

Une

mpl

oyed

* E

mpl

oyed

or s

elf-

empl

oyed

(1)

–2.

146

4º1.

836º

Inac

tive

(2)

–1.

451

–C

onne

ctio

n to

the

Inte

rnet

Mob

ile b

road

band

(yes

)1.

472

9º–

– F

ixed

bro

adba

nd (y

es)

1.58

97º

1.23

410

º–


1 3

Tabl

e 8

(con

tinue

d)

Dep

ende

nt v

aria

ble:

pur

chas

ing

acco

m-

mod

atio

n an

d tra

vel v

ia th

e In

tern

etLe

ss d

evel

oped

regi

ons

Regi

ons i

n tra

nsiti

onM

ost d

evel

oped

regi

ons

Odd

s rat

ioW

eigt

h or

der*

*O

dds r

atio

Wei

gth

orde

r**

Odd

s rat

ioW

eigt

h or

der*

*

Con

nect

ion

to th

e In

tern

et v

ia m

obile

ne

twor

k or

USB

out

side

the

hom

e in

la

st 3

mon

ths (

yes)

1.53

48º

1.18

71.

298

10º

Con

nect

ion

to th

e In

tern

et b

y W

i-Fi o

ut-

side

the

hom

e in

last

3 m

onth

s (ye

s)1.

432

10º

1.61

38º

1.55

18º

Dig

ital s

kills

glo

bal

Non

e or

low

leve

l* B

asic

leve

l (1)

1.62

1.33

41.

228

Hig

her l

evel

(2)

2.41

12º

1.94

36º

2.00

35º

Use

of t

he In

tern

et L

ooki

ng fo

r inf

orm

atio

n in

last

3 m

onth

s (y

es)

–1.

615

7º1.

498

9º

Com

mun

icat

ion

in la

st 3

mon

ths (

yes)

––

– S

ocia

l med

ia in

last

3 m

onth

s (ye

s)–

––

E-c

omm

erce

in la

st 12

mon

ths (

yes)

17.1

871º

9.47

81º

16.8

81º

E-g

over

nmen

t in

last

12 m

onth

s (ye

s)1.

726

6º1.

963

5º2.

004

4º E

-lear

ning

in la

st 12

mon

ths (

yes)

1.18

812

º–

1.13

113

ºSe

ller’s

orig

in: p

urch

ase

12 m

onth

s S

elle

r out

side

the

EU (y

es)

1.79

95º

1.47

79º

1.74

57º

Sour

ce: o

wn

rese

arch

on

the

basi

s of t

he lo

gisti

c re

gres

sion

resu

lts fo

r eac

h re

gion

– St

atist

ical

ly in

sign

ifica

nt*T

he v

aria

ble

with

the

high

est o

dds r

atio

has

bee

n ch

osen

for v

aria

bles

with

seve

ral s

tatis

tical

ly si

gnifi

cant

cat

egor

ies

**O

rder

of i

nflue

nce

only

for c

ateg

orie

s with

a h

ighe

r odd

s rat

io

1 3


6 Conclusions

The study has brought to light the changes in behavioural patterns in the different European areas on the basis of their economic development. The discussion has sep-arated the various factors and revealed the findings by types of region. This would be the first conclusion: digital behaviour and the factors making that behaviour more likely in the field of e-tourism, vary depending on economic development, and it is possible to detect a degree of divergence in behaviour on a European level.

It is also clear that having carried out e-commerce activities beforehand in other areas is by far the most important factor. Any other factor is irrelevant when com-pared to the importance of having prior on-line experience. This finding amounts to a clarifying observation, setting and this study apart from other research approaches that focus excessively on socioeconomic factors, and contributing to the amount of research about the uncertainty regarding on-line tourism booking, transaction secu-rity and trust (Wu and Chang 2005). Our results are aligned with the model devised by Gursoy and McCleary (2004), which proposes that familiarity and expertise, learning and previous visits have an indirect influence on the search for immedi-ate pre-purchase information needs, in an online shopping environment, prior online purchase experience helps to dispel uncertainties and eventually leads to an increase in customer purchase intention (Thamizhvanan and Xavier 2013).

The importance of age, connectivity, acquiring digital skills and the way the Internet is used are all secondary factors that have effects varying from one region to another. In any case, one major finding is the role of the senior segment. There-fore, our study includes this important finding, since little research has been done into the influence of the Internet and the type of product acquired in the senior mar-ket (Vigolo and Confente 2013). On the other hand, being a woman increases the probability of deciding to purchase tourism via the Internet only in the More Devel-oped Regions, therefore, linking economic development with gender could be an interesting subject of future research. Furthermore, the e-tourist profile seems to be dynamic, in the sense that e-tourists acquire expertise and competence, which serves as an incentive to keep on researching.

Our study conducted a pan-European analysis, which was the first time that par-ticular approach was made to the subject, because in the past such research had always concentrated on specific countries. It is an approach that enables us to have a comprehensive overview of the situation for the EU as a whole.

This research work has certain limitations, most of which arise from the charac-teristics inherent to the Eurostat questionnaire, making it difficult to obtain stand-ardised data for all the countries and to incorporate a large amount of dichotomous variables. The amount of variables together with the very high number of registers, greatly complicates a quantitative analysis. Furthermore, the analysis only focused on the early phases of the e-tourism process, but ICT is also present when tourists are already travelling (Bai 2015), or to inform about their travel experiences after the journey (Morrison et al. 2001; Kim et al. 2009; Ho and Lee 2015).

In spite of this, the genuine observations included here provide new insights into the reality of the European tourist in the digital era, and somehow extends the scope


1 3

of the usual eTourism marketing studies on the profile of the digital tourist. Con-sidering the fact that there are very few studies on this subject that include a multi-country perspective, the article contributes to the academic literature on a question that is very important to the EU economy: it open an approach that is more ambi-tious, from a geographical viewpoint, and links the question to regional develop-ment, which is an original contribution that deserves focused research.

The huge amount of data provided by the responses to the questionnaire has opened up new avenues of research. Such information will help researchers to obtain greater in-depth knowledge about the different aspects mentioned here or to consider other methodologies for analysing and comparing the data. We propose that this pan-European approach should be explored still further, in view of the importance of tourism to Europe, particularly e-tourism, which is one of Europe’s major sources of wealth in the 21st Century.

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Dynamics of digital tourism’s consumers in the EU · 2019-10-01 · Luis Manuel Ruiz‑Gómez1 ·...

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